Discrete event control systems, such as an automated industrial control system, generally control a machine having a large number of components (e.g., sensors and actuators), which may malfunction due to transient errors and other hard or soft failures. Because of the immense number of possible failure points in the machine, attempts have been made to provide control systems that automatically diagnose the malfunction and pinpoint the failure point, thus reducing costly down-time of the industrial plant.
Known systems have approached the diagnostic problem with varying success. For example, the diagnostic engines of prior art systems often are based on state-machine models that can detect only certain hard failures. Thus, transient errors and the erroneous occurrence of events are not diagnosed and predictions of malfunctions are not feasible. Further, such diagnostic engines often must be explicitly programmed. Or, if the engine is capable of autonomously learning the behavior of a machine, the learning session often is based on data gathered while the machine is operating in one machine state, in a fixed environmental condition, and at the beginning of the life of the machine. Accordingly, real-time changes in the behavior of the machine, that may be due to environmental conditions or the natural wear and aging process, are often erroneously diagnosed as malfunctions. To be able to take the various operating conditions into account, the diagnostic engine must either undergo a lengthy reprogramming process or be subjected to a new learning session.
Prior art systems also generally are incapable of discerning the optimum state-machine model to use for developing the rules to diagnose the behavior of the machine. For example, the state-machine model will include a number of known sequential and temporal patterns which indicate the proper occurrences of the various discrete events associated with the manufacturing process. The diagnostic engine, however, may indiscriminately develop diagnostic rules based on these patterns. Thus, a particular rule may be based on a pattern corresponding to a known causal relationship between events, a pattern including a sequence of a large number of discrete events, or a pattern including a long time interval between discrete events. Each of these scenarios presents disadvantages and inefficiencies. In particular, restraining diagnostic rules to known causal relationships prevents the engine from selecting non-intuitive timing patterns that may produce simpler, more efficient rules. Moreover, a long sequential pattern necessitates the use of a larger amount of memory to store the occurrences of the multiple discrete events in the pattern and consumes more computing power, while a rule based on a long temporal pattern may result in a tardy diagnosis of a machine malfunction. Further, known diagnostic engines typically are not capable of determining the minimum number of patterns necessary to adequately diagnose the machine's behavior and predict malfunctions or of judging which patterns provide the most reliable indicators of the machine's health.
Accordingly, it would be desirable to develop a versatile diagnostic engine for discrete event control systems capable of discriminately developing diagnostic rules for diagnosing the behavior of an automated machine. The diagnostic engine would not be restricted by known causal relationships and, thus, could autonomously select and learn the optimum discrete event patterns for reliably diagnosing and predicting the behavior of the machine. Moreover, the diagnostic engine would be capable of automatically adapting to changed operating conditions of the machine, such as environmental variations, modifications to the machine, wear and aging of the machine, and different machine states.